CN111259942A - Method for detecting weak target in water - Google Patents
Method for detecting weak target in water Download PDFInfo
- Publication number
- CN111259942A CN111259942A CN202010026118.7A CN202010026118A CN111259942A CN 111259942 A CN111259942 A CN 111259942A CN 202010026118 A CN202010026118 A CN 202010026118A CN 111259942 A CN111259942 A CN 111259942A
- Authority
- CN
- China
- Prior art keywords
- data
- vector
- reconstruction
- dimension
- time
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
- G06F18/2411—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on the proximity to a decision surface, e.g. support vector machines
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/25—Fusion techniques
- G06F18/253—Fusion techniques of extracted features
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V2201/00—Indexing scheme relating to image or video recognition or understanding
- G06V2201/07—Target detection
Abstract
The invention provides a method for detecting a weak target in water, which is a method for extracting various characteristic quantities such as relative change entropy, correlation dimension, Lyapunov index and the like of data, mapping the various characteristic quantities to the same high latitude characteristic space by using an SVM (support vector machine) method and finally detecting the weak target in water through a classification result of the SVM. The invention utilizes a relative change entropy analysis method to analyze the relative change of the difference of two different progressive exponential rates of the received data and characterize the degree of the change, thereby effectively avoiding the interference caused by the energy fluctuation of the environmental noise. Meanwhile, the change of the nonlinear characteristics of data is extracted from other measurements by using analysis methods such as correlation dimension, Lyapunov index and the like, the interference caused by pulse components in environmental noise is avoided by using a feature fusion method, the remote detection of the underwater target without prior information can be realized, and the method has the advantages of long detection distance, no need of prior information and the like.
Description
Technical Field
The invention relates to the technical field of underwater acoustic signal processing, in particular to a target detection method.
Background
In order to deal with new opportunities and challenges brought by the 'ocean century' and further revive the ocean industry, ocean science and technology are developed according to the strategic idea of 'building ocean strong nations' to improve the ocean defense strength of China, the research of the ocean safety field is of great importance, and the detection of weak targets in water is an important research direction of the ocean safety field.
The detection method of the weak target in water is line spectrum detection, and the common methods in line spectrum detection include an autocorrelation detection method, a fast fourier transform method, a self-adaptive line spectrum enhancement method and the like, but the methods mostly need to obtain prior information such as line spectrum frequency and the like, and the detection result is greatly influenced without the prior information. Meanwhile, due to the high-speed development of the sound stealth technology and the continuous application of technologies such as active vibration isolation, active damping, acoustic intelligent structures and the like of mechanical equipment, the line spectrum of the radiation noise of the weak target in water is well controlled, the amplitude of the line spectrum becomes very small, the energy of the line spectrum is greatly reduced, and even the quantity is controllable.
Zhang Anqing et al found that the ocean background noise was accompanied by non-Gaussian characteristics of significant pulses by statistical analysis of the characteristics of a large amount of actually measured ocean background noise data. This noise, which is of an impulse nature, can cause variations in the amplitude of certain eigenvalue values, thereby causing false alarms and resulting in reduced detection performance.
Aiming at the problems that the prior information of a characteristic line spectrum is difficult to obtain, false alarm interference caused by pulse noise and the like, and the requirement of nonlinear characteristic representation under a complex marine environment is combined, the invention provides a method for remotely detecting the underwater target without any prior information by extracting the inherent nonlinear characteristic in target radiation noise, and a plurality of characteristics are fused by utilizing an SVM (support vector machine), so that the false alarm caused by the pulse component in background noise is avoided, the performance of target detection is further improved, and the applicability of the method is improved.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides a method for detecting weak targets in water. Aiming at the problems of complex and various background noises, high low signal-to-noise ratio detection difficulty, difficulty in acquiring prior information, false alarm interference of pulse noise and the like in underwater weak signal detection, the method is provided for mapping various characteristic quantities to the same high-latitude characteristic space by extracting various characteristic quantities of data such as relative change entropy, correlation dimension, Lyapunov index and the like and utilizing an SVM (support vector machine) method, and finally realizing the detection of the underwater weak target through the classification result of the SVM. The invention utilizes the chaos theory to accurately describe and realize the extraction of nonlinear characteristics in target signals and environmental noise, realizes the low signal-to-noise ratio detection of weak targets in water under the condition of lacking prior information, and utilizes the method of different characteristics and characteristic fusion of various characteristic quantities to effectively avoid false alarm caused by pulse components in background noise.
Aiming at the problems that the prior information of a characteristic line spectrum is difficult to obtain, false alarm interference caused by pulse noise and the like, and the requirement of nonlinear characteristic representation under a complex marine environment is combined, the invention provides a remote underwater target detection method without any prior information by extracting the inherent nonlinear characteristic in target radiation noise, and a plurality of characteristics are fused by utilizing an SVM (support vector machine), so that the false alarm caused by pulse components in background noise is avoided, the performance of target detection is further improved, and the applicability of the method is improved.
The technical scheme adopted by the invention for solving the technical problem comprises the following steps:
the method comprises the following steps: acquiring an acoustic signal in the sea by using a hydrophone, wherein the acoustic signal is marked as a (t), namely an input signal, and processing the input signal according to the following formula (1) to change the variance into 1;
wherein: a (t)i) Is the ith data of the acoustic signal a (t), and N' is the number of data of the acoustic signal a (t);
step two: performing phase space reconstruction on s (t) in the step one according to a Takens reconstruction theorem;
and (3) solving the reconstruction dimension and the time delay of s (t) by utilizing a G-P algorithm, and finally completing the phase space reconstruction of s (t):
S(ti)＝[s(ti),s(ti+τ),s(ti+2τ),…,s(ti+(m-1)τ)](2)
where S (t) denotes the input signal, S (t)i) Representing the ith column of the reconstructed signal, wherein tau is delay time, m is embedding dimension, and i is a subscript of data;
step three: calculating a relative change entropy value of S (t) after phase space reconstruction, and specifically comprising the following steps:
performing phase space reconstruction by taking N data received at initial time as reference data, wherein N is the number of data during data processing, and obtaining reconstructed data S1(t) then S is treated according to the following formula (3)1(t) is differentiated to obtain S'1(t), finally obtaining S'1The singular value of (t) is taken as a reference vector A;
wherein: j is 1, 2 … m represents the data dimension value after reconstruction, T represents the data utilization rate, i is 1, 2 … N-1 represents the subscript of some dimension data after reconstruction, S1(t) is reconstructed data, S'1(t) is S1(t) the data after the differentiation,
the (i + 1) th data in the j dimension of the reconstructed data;
taking data received after N data at initial time as data S to be measured2(t) performing phase space reconstruction, and obtaining S according to the formula (3) after reconstruction is completed2Differential S of (t)'2(t) and obtaining S'2The singular value of (t) is recorded as a vector B to be detected;
processing the vector B to be detected and the reference vector A according to the formula (4) to obtain a relative vector C:
obtaining a junction according to equation (5)Relative change entropy H of received data0：
Wherein: i-1, 2 … m, representing the subscripts of the elements in the respective vector, g (C) representing the product of m elements in a relative vector C, tdFor a time period of fixed length, N0＝td×T÷N，H0For a fixed time period tdRelative entropy of change of the inner data;
step four: calculate S (t)i) Maximum Lyapunov exponent of (d);
the maximum Lyapunov exponent was calculated using equation (7):
where M is the number of iterations, LiIs tiTime S (t)i) And neighboring point S1(ti) L 'of'iIs tiTime S (t)i) And neighboring point S1(ti) After the fixed time evolution, only the distance greater than L is reserved for solving the maximum Lyapunov indexiL'i， t0Is an initial time tMIs the time after iterating M times from the initial time;
step five: calculate S (t)i) The correlation dimension value of (a);
the correlation dimension is calculated using equations (8) and (9):
wherein C isn(r) is a correlation integral calculated by the formula (9), θ is a Heaviside unit function, K ═ N- (m-1) τ is the number of vector points in the reconstructed phase space, and r is a vector point pitch value;
wherein, DGP is a correlation dimension numerical value;
step six: constructing an underwater multi-target data set;
taking data of background noise and target radiation noise in water recorded by a hydrophone as a sample set, and respectively solving three characteristic values of relative change entropy, Lyapunov index and correlation dimension of the sample set by utilizing the steps from the first step to the fifth step, and recording the three characteristic values as L ═ X { (X)3,Y3}；
Wherein: x3For the training set, Y3For the test set, L is the total set of eigenvalues;
mixing X3＝{x1,x2,x3Sending the training data to an SVM classifier for training as a training set, and Y3＝{y1,y2,y3Sending the test set to an SVM classifier for testing;
step seven: and (4) detecting by using an SVM (support vector machine) tester, and taking a classification result given by a test set of the multi-class SVM classifier as a final detection result of the weak target in the water.
The method for detecting the weak target in the water has the advantages that the relative change of the difference between two different progressive exponential rates of the received data is analyzed by using a relative change entropy analysis method, the change degree of the difference is characterized, and the interference caused by the energy fluctuation of the environmental noise can be effectively avoided. Meanwhile, the change of the nonlinear characteristics of the data is extracted from other measurements by using analysis methods such as correlation dimension, Lyapunov index and the like, and finally, the interference caused by pulse components in the environmental noise is further avoided by using a feature fusion method. Therefore, the method is used for detecting the underwater target, can realize the remote detection of the underwater target without prior information, and has the advantages of long detection distance, no need of prior information and the like.
Drawings
Fig. 1 is a flow chart of a route detection method of the present invention.
FIG. 2 is a relative entropy of change technique route of the present invention.
Fig. 3 is a diagram of the SVM detection result of the present invention.
Detailed Description
The invention is further illustrated with reference to the following figures and examples.
The flow chart of the present invention is shown in fig. 1.
The method comprises the following steps: acquiring an acoustic signal in the sea by using a hydrophone, wherein the acoustic signal is marked as a (t), namely an input signal, and processing the input signal according to the following formula (1) to change the variance into 1;
wherein: a (t)i) Is the ith data of the acoustic signal a (t), and N' is the number of data of the acoustic signal a (t);
step two: performing phase space reconstruction on s (t) in the step one according to a Takens reconstruction theorem;
and (3) solving the reconstruction dimension and the time delay of s (t) by utilizing a G-P algorithm, and finally completing the phase space reconstruction of s (t):
S(ti)＝[s(ti),s(ti+τ),s(ti+2τ),…,s(ti+(m-1)τ)](2)
where S (t) denotes the input signal, S (t)i) Representing the ith column of the reconstructed signal, wherein tau is delay time, m is embedding dimension, and i is a subscript of data;
step three: calculating the relative change entropy value of s (t) after the phase space reconstruction, as shown in fig. 2, the specific steps are:
performing phase space reconstruction by taking N data received at the initial moment as reference data, and performing phase space reconstruction according to the step one and the step two, wherein N is the number of data during data processing, N is 4000 in the invention, and the reconstructed data S is obtained1(t) then S is treated according to the following formula (3)1(t) is differentiated to obtain S'1(t), finally obtaining S'1The singular value of (t) is taken as a reference vector A;
wherein: j is 12 … m represents a data dimension value after reconstruction, T represents a data utilization rate, i is 1, 2 … N-1 represents a subscript of one dimension of data after reconstruction, and S is1(t) is reconstructed data, S'1(t) is S1(t) the data after the differentiation,
the (i + 1) th data in the j dimension of the reconstructed data;
taking data received after N data at initial time as data S to be measured2(t) performing phase space reconstruction, and obtaining S according to the formula (3) after reconstruction is completed2Differential S of (t)'2(t) and obtaining S'2The singular value of (t) is recorded as a vector B to be detected;
processing the vector B to be detected and the reference vector A according to the formula (4) to obtain a relative vector C:
the relative change entropy H of the received data is determined according to equation (5)0：
Wherein: i-1, 2 … m, representing the subscripts of the elements in the respective vector, g (C) representing the product of m elements in a relative vector C, tdFor a fixed length period of time (e.g. 1 second), N0＝td×T÷N，H0For a fixed time period tdRelative entropy of change of the inner data;
step four: calculate S (t)i) Maximum Lyapunov exponent of (d);
the maximum Lyapunov exponent was calculated using equation (7):
where M is the number of iterations, LiIs tiTime S (t)i) And neighboring point S1(ti) L 'of'iIs tiTime S (t)i) And neighboring point S1(ti) After a fixed time (such as 0.01 second) evolves, only the interval larger than L is reserved for solving the maximum Lyapunov indexiL'iT0 is the initial time, tM is the time after iterating M times from the initial time;
step five: calculate S (t)i) The correlation dimension value of (a);
the correlation dimension is calculated using equations (8) and (9):
wherein C isn(r) is a correlation integral calculated by the formula (9), θ is a Heaviside unit function, K ═ N- (m-1) τ is the number of vector points in the reconstructed phase space, and r is a vector point pitch value;
wherein, DGP is a correlation dimension numerical value;
step six: constructing an underwater multi-target data set;
taking data of background noise and target radiation noise in water recorded by a hydrophone as a sample set, and respectively solving three characteristic values of relative change entropy, Lyapunov index and correlation dimension of the sample set by utilizing the steps from the first step to the fifth step, and recording the three characteristic values as L ═ X { (X)3,Y3}；
Wherein: x3For the training set, Y3For the test set, L is the total set of eigenvalues;
mixing X3＝{x1,x2,x3Sending the training data to an SVM classifier for training as a training set, and Y3＝{y1,y2,y3Sending the test set to an SVM classifier for testing; the division of the training set and the test set is to use the cvpartition function, and the cvpartition function is used for training according to 70 percent and is used for automatically dividing the cvpartition function into 30 percent for testing;
step seven: and (4) detecting by using an SVM (support vector machine) tester, and taking a classification result given by a test set of the multi-class SVM classifier as a final detection result of the weak target in the water.
As shown in fig. 3, the SVM has a target for the description that the decision result is 1, and the description of-1 has no target, wherein the SVM prediction value is the result given by the decider and the actual flag value is the actual situation on the data.
Claims (1)
1. A method for detecting a weak target in water is characterized by comprising the following steps:
the method comprises the following steps: acquiring an acoustic signal in the sea by using a hydrophone, wherein the acoustic signal is marked as a (t), namely an input signal, and processing the input signal according to the following formula (1) to change the variance into 1;
wherein: a (t)i) Is the ith data of the acoustic signal a (t), and N' is the number of data of the acoustic signal a (t);
step two: performing phase space reconstruction on s (t) in the step one according to a Takens reconstruction theorem;
and (3) solving the reconstruction dimension and the time delay of s (t) by utilizing a G-P algorithm, and finally completing the phase space reconstruction of s (t):
S(ti)＝[s(ti),s(ti+τ),s(ti+2τ),…,s(ti+(m-1)τ)](2)
where S (t) denotes the input signal, S (t)i) Representing the ith column of the reconstructed signal, wherein tau is delay time, m is embedding dimension, and i is a subscript of data;
step three: calculating a relative change entropy value of S (t) after phase space reconstruction, and specifically comprising the following steps:
performing phase space reconstruction by taking N data received at initial time as reference data, wherein N is the number of data during data processing, and obtaining reconstructed data S1(t) then S is treated according to the following formula (3)1(t) is differentiated to obtain S'1(t), finally obtaining S'1The singular value of (t) is taken as a reference vector A;
wherein: j is 1, 2 … m represents the data dimension value after reconstruction, T represents the data utilization rate, i is 1, 2 … N-1 represents the subscript of some dimension data after reconstruction, S1(t) is reconstructed data, S'1(t) is S1(t) the data after the differentiation,
the (i + 1) th data in the j dimension of the reconstructed data;
taking data received after N data at initial time as data S to be measured2(t) performing phase space reconstruction, and obtaining S according to the formula (3) after reconstruction is completed2Differential S of (t)'2(t) and obtaining S'2The singular value of (t) is recorded as a vector B to be detected;
processing the vector B to be detected and the reference vector A according to the formula (4) to obtain a relative vector C:
the relative change entropy H of the received data is determined according to equation (5)0：
Wherein: i-1, 2 … m, representing the subscripts of the elements in the respective vector, g (C) representing the product of m elements in a relative vector C, tdFor a time period of fixed length, N0＝td×T÷N，H0For a fixed time period tdRelative entropy of change of the inner data;
step four: calculate S (t)i) Maximum Lyapunov exponent of (d);
the maximum Lyapunov exponent was calculated using equation (7):
where M is the number of iterations, LiIs tiTime S (t)i) And neighboring point S1(ti) L 'of'iIs tiTime S (t)i) And neighboring point S1(ti) After the fixed time evolution, only the distance greater than L is reserved for solving the maximum Lyapunov indexiL'i，t0Is an initial time tMIs the time after iterating M times from the initial time;
step five: calculate S (t)i) The correlation dimension value of (a);
the correlation dimension is calculated using equations (8) and (9):
wherein C isn(r) is a correlation integral calculated by the formula (9), θ is a Heaviside unit function, K ═ N- (m-1) τ is the number of vector points in the reconstructed phase space, and r is a vector point pitch value;
wherein, DGP is a correlation dimension numerical value;
step six: constructing an underwater multi-target data set;
and taking data of background noise and target radiation noise in water recorded by the hydrophone as a sample set, and respectively solving three characteristic values of relative change entropy, Lyapunov index and correlation dimension of the sample set by utilizing the steps from the first step to the fifth step, and recording the characteristic values as L ═ X { (X)3,Y3}；
Wherein: x3For the training set, Y3For the test set, L is the total set of eigenvalues;
mixing X3＝{x1,x2,x3Sending the training data to an SVM classifier for training as a training set, and Y3＝{y1,y2,y3Sending the test set to an SVM classifier for testing;
step seven: and (4) detecting by using an SVM (support vector machine) tester, and taking a classification result given by a test set of the multi-class SVM classifier as a final detection result of the weak target in the water.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010026118.7A CN111259942B (en) | 2020-01-10 | 2020-01-10 | Method for detecting weak target in water |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010026118.7A CN111259942B (en) | 2020-01-10 | 2020-01-10 | Method for detecting weak target in water |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111259942A true CN111259942A (en) | 2020-06-09 |
| CN111259942B CN111259942B (en) | 2022-04-26 |
Family
ID=70948663
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010026118.7A Active CN111259942B (en) | 2020-01-10 | 2020-01-10 | Method for detecting weak target in water |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111259942B (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112711026A (en) * | 2020-12-19 | 2021-04-27 | 西北工业大学 | Target detection and signal frequency estimation method based on variable differential step length relative change entropy |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6897802B1 (en) * | 2003-11-10 | 2005-05-24 | Raytheon Company | Fusion of shape and multiscale features for unknown target rejection |
| CN103810499A (en) * | 2014-02-25 | 2014-05-21 | 南昌航空大学 | Application for detecting and tracking infrared weak object under complicated background |
| CN103871058A (en) * | 2014-03-12 | 2014-06-18 | 北京航空航天大学 | Compressed sampling matrix decomposition-based infrared small target detection method |
| CN105894033A (en) * | 2016-04-01 | 2016-08-24 | 大连理工大学 | Weak target detection method and weak target detection system under background of sea clutter |
| CN106682615A (en) * | 2016-12-28 | 2017-05-17 | 西北工业大学 | Method for detecting underwater dim small target |
| CN108876021A (en) * | 2018-05-31 | 2018-11-23 | 华中科技大学 | A kind of Medium-and Long-Term Runoff Forecasting method and system |
| CN110060700A (en) * | 2019-03-12 | 2019-07-26 | 上海微波技术研究所（中国电子科技集团公司第五十研究所） | Short sequence audio analysis method based on parametric spectral estimation |
-
2020
- 2020-01-10 CN CN202010026118.7A patent/CN111259942B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6897802B1 (en) * | 2003-11-10 | 2005-05-24 | Raytheon Company | Fusion of shape and multiscale features for unknown target rejection |
| CN103810499A (en) * | 2014-02-25 | 2014-05-21 | 南昌航空大学 | Application for detecting and tracking infrared weak object under complicated background |
| CN103810499B (en) * | 2014-02-25 | 2017-04-12 | 南昌航空大学 | Application for detecting and tracking infrared weak object under complicated background |
| CN103871058A (en) * | 2014-03-12 | 2014-06-18 | 北京航空航天大学 | Compressed sampling matrix decomposition-based infrared small target detection method |
| CN105894033A (en) * | 2016-04-01 | 2016-08-24 | 大连理工大学 | Weak target detection method and weak target detection system under background of sea clutter |
| CN106682615A (en) * | 2016-12-28 | 2017-05-17 | 西北工业大学 | Method for detecting underwater dim small target |
| CN108876021A (en) * | 2018-05-31 | 2018-11-23 | 华中科技大学 | A kind of Medium-and Long-Term Runoff Forecasting method and system |
| CN110060700A (en) * | 2019-03-12 | 2019-07-26 | 上海微波技术研究所（中国电子科技集团公司第五十研究所） | Short sequence audio analysis method based on parametric spectral estimation |
Non-Patent Citations (5)
| Title |
|---|
| HONGHUI YANG 等: "Underwater Acoustic Target Recognition Using SVM Ensemble via Weighted Sample and Feature Selection", 《PROCEEDINGS OF 2016 13TH INTERNATIONAL BHURBAN CONFERENCE ON APPLIED SCIENCES & TECHNOLOGY》 * |
| JONGKWON CHOI 等: "Acoustic Classification of Surface and Underwater Vessels in the Ocean Using Supervised Machine Learning", 《SENSORS》 * |
| SHILEI MA 等: "Stochastic Resonance for Underwater VLF Weak Signal Detection under L´evy Noise Background", 《2017 IEEE》 * |
| 张建 等: "基于固有模态奇异值熵的微弱目标检测算法", 《现代雷达》 * |
| 许小可: "基于非线性分析的海杂波处理与目标检测", 《万方数据库》 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112711026A (en) * | 2020-12-19 | 2021-04-27 | 西北工业大学 | Target detection and signal frequency estimation method based on variable differential step length relative change entropy |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111259942B (en) | 2022-04-26 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN104166804B (en) | A kind of operation mode discrimination method based on time-frequency domain list source point sparse component analysis | |
| CN104569948B (en) | Sub-band adaptive GLRT LTD detection methods under sea clutter background | |
| CN103293515B (en) | Ship and warship line spectrum noise source longitudinal distribution characteristic measuring method | |
| US9961460B2 (en) | Vibration source estimation device, vibration source estimation method, and vibration source estimation program | |
| CN105589066B (en) | A kind of method that underwater uniform motion ROV parameter is estimated using vertical vector battle array | |
| RU2653585C1 (en) | Method of detecting the noise-producing, moving in the sea objects | |
| CN104678384B (en) | Method for estimating underwater target speed by using sound pressure difference cross-correlation spectrum analysis of beam fields | |
| CN110376575B (en) | Low-frequency line spectrum detection method based on damping parameter matching stochastic resonance | |
| CN104502904B (en) | Torpedo homing beam sharpening method | |
| CN111257943B (en) | Shallow sea low-frequency sound field time-frequency interference fringe extraction method | |
| CN111259942B (en) | Method for detecting weak target in water | |
| CN110865375B (en) | Underwater target detection method | |
| US20220317273A1 (en) | Underwater acoustic target ranging method based on feature extraction and neural network | |
| CN116449351A (en) | Active sonar processing method and system based on COSTAS waveform | |
| TW202030496A (en) | Signal processing method | |
| CN113126029B (en) | Multi-sensor pulse sound source positioning method suitable for deep sea reliable acoustic path environment | |
| Le Bot et al. | Cross recurrence plot analysis based method for tdoa estimation of underwater acoustic signals | |
| CN108562905B (en) | Underwater target detection method based on modal domain subspace detector weighting | |
| CN113158907A (en) | Weak ship radiation characteristic signal detection method based on wavelet and chaos theory | |
| CN112684437A (en) | Passive distance measurement method based on time domain warping transformation | |
| Guo et al. | Tracking-positioning of sound speed profiles and moving acoustic source in shallow water | |
| Garnier et al. | Applications of random matrix theory for sensor array imaging with measurement noise | |
| CN112711026B (en) | Target detection and signal frequency estimation method based on variable differential step length relative change entropy | |
| CN114578333B (en) | Active sonar target dynamic and static identification method | |
| Lu et al. | Array shape estimation method based on extraction of single normal mode |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |